How can you evaluate transportation modes for scalability and efficiency?

Demand and capacity will disproportionately impact cost. Recently, we decided to build bigger bucket truck bodies for our fleet after re-evaluating fluctuating critical cost centers like fueling, maintenance and other operational costs. Freight value on lower-ton deliveries over same distances were too expensive despite the consistency in demand, but even more costly was the use of alternative modes because of the cargo value when compared to the modal option.

We cant talk about scalability and efficiency without mentioning Cost-effectiveness. Meaning that we have to analyze the total cost of using the transportation mode, including initial investment, operational costs, maintenance, and any hidden expenses. Then compare these costs with the benefits it provides.

One thing I have found helpful is to break the cost down to the units (per ton or per km or both) for different transportation modes. This helps to see which transportation mode is cost-effective whilst ensuring you deliver quality to both your internal and external customers.

The key element here is consistency. In a fast changing environment with a deluge of economic emergencies, the need to regularly review and update data will afford the insight required to ultimately ensure the efficiency and scalability of a transport mode. One time at work, we had relied on a one-week old data to prepare our budget and immediately aligned it to our short-term growth goals. It would be a barrage of challenges affecting our entire business operations for the next six weeks because of the shocking changes unique to our preferred mode. Our only luck was a loophole clause in our contract with our customer to which we resorted for succor.

An extension is synchromodal optimisation - dynamic planning and updating choice of mode based on real-time events. Many freight movements are multimodal e.g. a combination of ship, barge, rail and truck. In a synchromodal operation the route and choice of modes may change, even for in-transit movements. For example a container might be scheduled to move via ship->barge->truck, but the ship is delayed. To catch up, the synchromodal algorithms might suggest using rail instead of barge, probably to a different terminal. To achieve this, good data availability is required, as well as sophisticated real-time optimisation algorithms that can cope with complex logistics networks. The ePIcenter project has made major advances in this area.